Longitudinal measurement invariance in prospective oral health-related quality of life assessment
Identifieur interne : 000C62 ( Pmc/Corpus ); précédent : 000C61; suivant : 000C63Longitudinal measurement invariance in prospective oral health-related quality of life assessment
Auteurs : Daniel R. Reissmann ; Mike T. John ; Leah Feuerstahler ; Kazuyoshi Baba ; Gyula Szab ; Asja Elebi ; Niels WallerSource :
- Health and Quality of Life Outcomes [ 1477-7525 ] ; 2016.
Abstract
Prospective assessments of oral health-related quality of life (OHRQoL) changes are prone to response shift effects when patients reconceptualize, reprioritize, or recalibrate the perceived meanings of OHRQoL test items. If this occurs, OHRQoL measurements are not “invariant” and may reflect changes in problem profiles or perceptions of OHRQoL test items. This suggests that response shift effects must be measured and controlled to achieve valid prospective OHRQoL measurement. The aim of this study was to quantify response shift effects of Oral Health Impact Profile (OHIP) scores in prospective studies of prosthodontic patients.
Data came from the Dimensions of Oral Health-Related Quality of Life Project. The final sample included 554 patients who completed the OHIP questionnaire on two occasions: pre- and post-treatment. Only items that compose the 14-item OHIP were analyzed. Structural equation models that included pre- and post-treatment latent factors of OHRQoL with different across-occasion constraints for factor loadings, intercepts, and residual variances were fit to the data using confirmatory factor analysis.
Data fit both the unconstrained model (RMSEA = .038, SRMR = .051, CFI = .92, TLI = .91) and the partially constrained model with freed residual variances (RMSEA = .037, SRMR = .064, CFI = .92, TLI = .92) well, meaning that the data are well approximated by a one-factor model at each occasion, and suggesting strong factorial across-occasion measurement invariance.
The results provided cogent evidence for the absence of response shift in single factor OHIP models, indicating that longitudinal OHIP assessments of OHRQoL measure similar constructs across occasions.
The online version of this article (doi:10.1186/s12955-016-0492-9) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12955-016-0492-9
PubMed: 27267885
PubMed Central: 4897855
Links to Exploration step
PMC:4897855Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Longitudinal measurement invariance in prospective oral health-related quality of life assessment</title><author><name sortKey="Reissmann, Daniel R" sort="Reissmann, Daniel R" uniqKey="Reissmann D" first="Daniel R." last="Reissmann">Daniel R. Reissmann</name><affiliation><nlm:aff id="Aff1">Department of Prosthetic Dentistry, Center for Dental and Oral Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN USA</nlm:aff></affiliation></author><author><name sortKey="John, Mike T" sort="John, Mike T" uniqKey="John M" first="Mike T." last="John">Mike T. John</name><affiliation><nlm:aff id="Aff2">Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN USA</nlm:aff></affiliation></author><author><name sortKey="Feuerstahler, Leah" sort="Feuerstahler, Leah" uniqKey="Feuerstahler L" first="Leah" last="Feuerstahler">Leah Feuerstahler</name><affiliation><nlm:aff id="Aff3">Department of Psychology, University of Minnesota, Minneapolis, USA</nlm:aff></affiliation></author><author><name sortKey="Baba, Kazuyoshi" sort="Baba, Kazuyoshi" uniqKey="Baba K" first="Kazuyoshi" last="Baba">Kazuyoshi Baba</name><affiliation><nlm:aff id="Aff4">Department of Prosthodontics, Showa University, Tokyo, Japan</nlm:aff></affiliation></author><author><name sortKey="Szab, Gyula" sort="Szab, Gyula" uniqKey="Szab G" first="Gyula" last="Szab">Gyula Szab</name><affiliation><nlm:aff id="Aff5">Department of Prosthodontics, University of Pécs, Pécs, Hungary</nlm:aff></affiliation></author><author><name sortKey=" Elebi, Asja" sort=" Elebi, Asja" uniqKey=" Elebi A" first="Asja" last=" Elebi">Asja Elebi</name><affiliation><nlm:aff id="Aff6">Department of Prosthodontics, University of Zagreb and Clinical Hospital Centre, Zagreb, Croatia</nlm:aff></affiliation></author><author><name sortKey="Waller, Niels" sort="Waller, Niels" uniqKey="Waller N" first="Niels" last="Waller">Niels Waller</name><affiliation><nlm:aff id="Aff3">Department of Psychology, University of Minnesota, Minneapolis, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27267885</idno><idno type="pmc">4897855</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897855</idno><idno type="RBID">PMC:4897855</idno><idno type="doi">10.1186/s12955-016-0492-9</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000C62</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000C62</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Longitudinal measurement invariance in prospective oral health-related quality of life assessment</title><author><name sortKey="Reissmann, Daniel R" sort="Reissmann, Daniel R" uniqKey="Reissmann D" first="Daniel R." last="Reissmann">Daniel R. Reissmann</name><affiliation><nlm:aff id="Aff1">Department of Prosthetic Dentistry, Center for Dental and Oral Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN USA</nlm:aff></affiliation></author><author><name sortKey="John, Mike T" sort="John, Mike T" uniqKey="John M" first="Mike T." last="John">Mike T. John</name><affiliation><nlm:aff id="Aff2">Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN USA</nlm:aff></affiliation></author><author><name sortKey="Feuerstahler, Leah" sort="Feuerstahler, Leah" uniqKey="Feuerstahler L" first="Leah" last="Feuerstahler">Leah Feuerstahler</name><affiliation><nlm:aff id="Aff3">Department of Psychology, University of Minnesota, Minneapolis, USA</nlm:aff></affiliation></author><author><name sortKey="Baba, Kazuyoshi" sort="Baba, Kazuyoshi" uniqKey="Baba K" first="Kazuyoshi" last="Baba">Kazuyoshi Baba</name><affiliation><nlm:aff id="Aff4">Department of Prosthodontics, Showa University, Tokyo, Japan</nlm:aff></affiliation></author><author><name sortKey="Szab, Gyula" sort="Szab, Gyula" uniqKey="Szab G" first="Gyula" last="Szab">Gyula Szab</name><affiliation><nlm:aff id="Aff5">Department of Prosthodontics, University of Pécs, Pécs, Hungary</nlm:aff></affiliation></author><author><name sortKey=" Elebi, Asja" sort=" Elebi, Asja" uniqKey=" Elebi A" first="Asja" last=" Elebi">Asja Elebi</name><affiliation><nlm:aff id="Aff6">Department of Prosthodontics, University of Zagreb and Clinical Hospital Centre, Zagreb, Croatia</nlm:aff></affiliation></author><author><name sortKey="Waller, Niels" sort="Waller, Niels" uniqKey="Waller N" first="Niels" last="Waller">Niels Waller</name><affiliation><nlm:aff id="Aff3">Department of Psychology, University of Minnesota, Minneapolis, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Health and Quality of Life Outcomes</title><idno type="eISSN">1477-7525</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Prospective assessments of oral health-related quality of life (OHRQoL) changes are prone to response shift effects when patients reconceptualize, reprioritize, or recalibrate the perceived meanings of OHRQoL test items. If this occurs, OHRQoL measurements are not “invariant” and may reflect changes in problem profiles or perceptions of OHRQoL test items. This suggests that response shift effects must be measured and controlled to achieve valid prospective OHRQoL measurement. The aim of this study was to quantify response shift effects of Oral Health Impact Profile (OHIP) scores in prospective studies of prosthodontic patients.</p></sec><sec><title>Methods</title><p>Data came from the Dimensions of Oral Health-Related Quality of Life Project. The final sample included 554 patients who completed the OHIP questionnaire on two occasions: pre- and post-treatment. Only items that compose the 14-item OHIP were analyzed. Structural equation models that included pre- and post-treatment latent factors of OHRQoL with different across-occasion constraints for factor loadings, intercepts, and residual variances were fit to the data using confirmatory factor analysis.</p></sec><sec><title>Results</title><p>Data fit both the unconstrained model (RMSEA = .038, SRMR = .051, CFI = .92, TLI = .91) and the partially constrained model with freed residual variances (RMSEA = .037, SRMR = .064, CFI = .92, TLI = .92) well, meaning that the data are well approximated by a one-factor model at each occasion, and suggesting strong factorial across-occasion measurement invariance.</p></sec><sec><title>Conclusions</title><p>The results provided cogent evidence for the absence of response shift in single factor OHIP models, indicating that longitudinal OHIP assessments of OHRQoL measure similar constructs across occasions.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Health Qual Life Outcomes</journal-id><journal-id journal-id-type="iso-abbrev">Health Qual Life Outcomes</journal-id><journal-title-group><journal-title>Health and Quality of Life Outcomes</journal-title></journal-title-group><issn pub-type="epub">1477-7525</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27267885</article-id><article-id pub-id-type="pmc">4897855</article-id><article-id pub-id-type="publisher-id">492</article-id><article-id pub-id-type="doi">10.1186/s12955-016-0492-9</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research</subject></subj-group></article-categories><title-group><article-title>Longitudinal measurement invariance in prospective oral health-related quality of life assessment</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Reissmann</surname><given-names>Daniel R.</given-names></name><address><email>d.reissmann@uke.de</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref></contrib><contrib contrib-type="author"><name><surname>John</surname><given-names>Mike T.</given-names></name><xref ref-type="aff" rid="Aff2"></xref></contrib><contrib contrib-type="author"><name><surname>Feuerstahler</surname><given-names>Leah</given-names></name><xref ref-type="aff" rid="Aff3"></xref></contrib><contrib contrib-type="author"><name><surname>Baba</surname><given-names>Kazuyoshi</given-names></name><xref ref-type="aff" rid="Aff4"></xref></contrib><contrib contrib-type="author"><name><surname>Szabó</surname><given-names>Gyula</given-names></name><xref ref-type="aff" rid="Aff5"></xref></contrib><contrib contrib-type="author"><name><surname>Čelebić</surname><given-names>Asja</given-names></name><xref ref-type="aff" rid="Aff6"></xref></contrib><contrib contrib-type="author"><name><surname>Waller</surname><given-names>Niels</given-names></name><xref ref-type="aff" rid="Aff3"></xref></contrib><aff id="Aff1"><label></label>Department of Prosthetic Dentistry, Center for Dental and Oral Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany</aff><aff id="Aff2"><label></label>Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN USA</aff><aff id="Aff3"><label></label>Department of Psychology, University of Minnesota, Minneapolis, USA</aff><aff id="Aff4"><label></label>Department of Prosthodontics, Showa University, Tokyo, Japan</aff><aff id="Aff5"><label></label>Department of Prosthodontics, University of Pécs, Pécs, Hungary</aff><aff id="Aff6"><label></label>Department of Prosthodontics, University of Zagreb and Clinical Hospital Centre, Zagreb, Croatia</aff></contrib-group><pub-date pub-type="epub"><day>7</day><month>6</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>7</day><month>6</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>14</volume><elocation-id>88</elocation-id><history><date date-type="received"><day>16</day><month>9</month><year>2015</year></date><date date-type="accepted"><day>31</day><month>5</month><year>2016</year></date></history><permissions><copyright-statement>© The Author(s). 2016</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>Prospective assessments of oral health-related quality of life (OHRQoL) changes are prone to response shift effects when patients reconceptualize, reprioritize, or recalibrate the perceived meanings of OHRQoL test items. If this occurs, OHRQoL measurements are not “invariant” and may reflect changes in problem profiles or perceptions of OHRQoL test items. This suggests that response shift effects must be measured and controlled to achieve valid prospective OHRQoL measurement. The aim of this study was to quantify response shift effects of Oral Health Impact Profile (OHIP) scores in prospective studies of prosthodontic patients.</p></sec><sec><title>Methods</title><p>Data came from the Dimensions of Oral Health-Related Quality of Life Project. The final sample included 554 patients who completed the OHIP questionnaire on two occasions: pre- and post-treatment. Only items that compose the 14-item OHIP were analyzed. Structural equation models that included pre- and post-treatment latent factors of OHRQoL with different across-occasion constraints for factor loadings, intercepts, and residual variances were fit to the data using confirmatory factor analysis.</p></sec><sec><title>Results</title><p>Data fit both the unconstrained model (RMSEA = .038, SRMR = .051, CFI = .92, TLI = .91) and the partially constrained model with freed residual variances (RMSEA = .037, SRMR = .064, CFI = .92, TLI = .92) well, meaning that the data are well approximated by a one-factor model at each occasion, and suggesting strong factorial across-occasion measurement invariance.</p></sec><sec><title>Conclusions</title><p>The results provided cogent evidence for the absence of response shift in single factor OHIP models, indicating that longitudinal OHIP assessments of OHRQoL measure similar constructs across occasions.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12955-016-0492-9) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>OHRQoL</kwd><kwd>OHIP</kwd><kwd>Measurement invariance</kwd><kwd>Response shift</kwd><kwd>Prospective studies</kwd><kwd>Longitudinal assessment</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000072</institution-id><institution>National Institute of Dental and Craniofacial Research</institution></institution-wrap></funding-source><award-id>R01DE022331</award-id><principal-award-recipient><name><surname>John</surname><given-names>Mike T.</given-names></name></principal-award-recipient></award-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001659</institution-id><institution>Deutsche Forschungsgemeinschaft</institution></institution-wrap></funding-source><award-id>RE 3289/2-1</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2016</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1"><title>Background</title><p>Oral health-related quality of life (OHRQoL) is an important patient-reported outcome in dentistry that characterizes the impact of oral diseases and dental treatments on quality of life. One of the most important tasks of an OHRQoL instrument is the measurement of change, that is, whether the patient’s situation has improved, stayed the same, or worsened. From a psychometric perspective, the measurement of change requires that a questionnaire measure the same construct (e.g., OHRQoL) on all occasions. Although this sounds simple, the relationships between questionnaire items and their underlying construct(s) may be complex. These relationships are typically characterized by a measurement model that need not stay constant across occasions. For instance, relative to a baseline, patients may change their internal standards of how they perceive OHRQoL when they are assessed at follow-up. In formal terms, a measurement model changes when, across measurement occasions, patients reconceptualize, reprioritize, or recalibrate the perceived meanings of test items [<xref ref-type="bibr" rid="CR1">1</xref>]. Reconceptualization occurs when patients’ concepts of OHRQoL, as indicated by OHRQoL test items, changes across occasions. [<xref ref-type="bibr" rid="CR2">2</xref>]. Reprioritization is defined as across-occasion variance in patient perceived importance of OHRQoL indicators. Finally, recalibration occurs when patients revise their internal standards of measurement. If any of these changes in the measurement model occurs, differences in perceived OHRQoL after treatment may reflect both changes in symptom profiles and changes in how patients perceive OHRQoL test items.</p><p>Measurement specialists have coined the term “response shift” [<xref ref-type="bibr" rid="CR3">3</xref>] to characterize the psychometric consequences of the above phenomena. When present but not statistically controlled, response shift effects can sully the measurement of quality of life. This notion is of more than theoretical interest because response shift effects have been demonstrated in several medical [<xref ref-type="bibr" rid="CR4">4</xref>–<xref ref-type="bibr" rid="CR6">6</xref>] and dental studies [<xref ref-type="bibr" rid="CR7">7</xref>–<xref ref-type="bibr" rid="CR9">9</xref>]. Nevertheless, the presence of response shift effects in the oral health domain remains to be unambiguously established.</p><p>The Oral Health Impact Profile (OHIP) [<xref ref-type="bibr" rid="CR10">10</xref>] is the most popular instrument for the assessment of OHRQoL. To improve measurement of change using the OHIP (and other OHRQoL instruments), response shift effects in prospective assessments need to be more accurately quantified to assess the true magnitude of dental intervention effects.</p><p>The aim of this study was to assess OHIP longitudinal measurement invariance by using structural equation models (SEM) to quantify response shift effects in pre- and post-treatment OHIP scores.</p></sec><sec id="Sec2"><title>Methods</title><sec id="Sec3"><title>Subjects, study design, and setting</title><p>The data for this secondary data analysis came from the Dimensions of Oral Health-Related Quality of Life (DOQ) Project [<xref ref-type="bibr" rid="CR11">11</xref>]. This project contains OHIP [<xref ref-type="bibr" rid="CR10">10</xref>] data from general population subjects and prosthodontics patients from six countries (Croatia, Germany, Hungary, Slovenia, Sweden, Japan). For the present study, only baseline and follow-up data from dental patients from Croatia, Hungary, Germany, and Japan undergoing prosthodontic treatments were available for analysis. Data from prosthodontic patients in Sweden included data from the first assessment only [<xref ref-type="bibr" rid="CR12">12</xref>, <xref ref-type="bibr" rid="CR13">13</xref>]. In Slovenia, patients received pre-treatment procedures for prosthodontic treatment (tooth pain was treated before more advanced dental therapy could be performed) [<xref ref-type="bibr" rid="CR14">14</xref>]. Therefore, data from Sweden and Slovenia could not be used in the analyses. The included samples consisted of patients in university-based prosthodontic departments. All research was conducted in accordance with accepted ethical standards for research practice. Written informed consent was obtained from all participants prior to their enrollment. For further information regarding study characteristics, sampling, inclusion and exclusion criteria, and prosthodontic treatments performed within the included patient populations, see original publications [<xref ref-type="bibr" rid="CR8">8</xref>, <xref ref-type="bibr" rid="CR15">15</xref>–<xref ref-type="bibr" rid="CR18">18</xref>].</p></sec><sec id="Sec4"><title>Assessment of oral health-related quality of life</title><p>Oral health-related quality of life was assessed using validated, language-specific versions of the OHIP [<xref ref-type="bibr" rid="CR19">19</xref>–<xref ref-type="bibr" rid="CR23">23</xref>]. Each OHIP item describes a situation that impacts OHRQoL and asks subjects to rate how often they experienced a specific impact within the last month. Responses occur on a 5-point scale with higher numbers indicating greater impact: 0 = ‘never’, 1 = ‘hardly ever’, 2 = ‘occasionally’, 3 = ‘fairly often’, and 4 = ‘very often.’</p><p>Analyses were conducted on the widely used OHIP-14 short version [<xref ref-type="bibr" rid="CR24">24</xref>]. OHIP-14 summary scores can range from 0 (no impact and best OHRQoL) to 56 (most impact and worst OHRQoL). In this manuscript, OHIP item numbers refer to the English-language 49-item OHIP version [<xref ref-type="bibr" rid="CR10">10</xref>]. At baseline, Cronbach’s alpha [<xref ref-type="bibr" rid="CR25">25</xref>] and the average inter-item correlations for the OHIP-14 data were .92 and .44, respectively. These values signal excellent reliability [<xref ref-type="bibr" rid="CR26">26</xref>, <xref ref-type="bibr" rid="CR27">27</xref>] for this brief OHRQoL questionnaire.</p><p>Overall, the number of missing responses was small (less than 1 %) in the DOQ Project [<xref ref-type="bibr" rid="CR11">11</xref>]. All OHIP-14 items were complete for 531 subjects (95.9 %) at baseline and for 538 subjects (97.1 %) at follow-up. Twenty-two subjects at baseline and twelve subjects at follow-up had one missing value, while two missing values were observed in one subject at baseline and four subjects at follow-up. Missing values were imputed using an individual’s median item response from the non-missing items of 49-item OHIP at each occasion.</p><p>Differences in OHIP-14 mean scores between baseline and follow-up were assessed using paired <italic>t</italic>-tests for the pooled study population and for each study separately.</p></sec><sec id="Sec5"><title>Establishing the measurement model</title><p>To evaluate across-occasion measurement invariance for the OHIP-14, we fit a series of <italic>a priori</italic> defined confirmatory factor analysis (CFA) [<xref ref-type="bibr" rid="CR28">28</xref>, <xref ref-type="bibr" rid="CR29">29</xref>] models and tested across-occasion measurement invariance following procedures outlined by Oort [<xref ref-type="bibr" rid="CR30">30</xref>] and Gregorich [<xref ref-type="bibr" rid="CR31">31</xref>]. Reconceptualization was evaluated by testing the dimensional and configural invariance of the measurement model. Reprioritization was assessed by testing metric invariance, and recalibration was evaluated by testing a model of strict factorial invariance. The CFA models included one common factor at each of the two assessment occasions because recent research suggests that, in many populations, OHIP item responses are well characterized by a single general factor [<xref ref-type="bibr" rid="CR32">32</xref>, <xref ref-type="bibr" rid="CR33">33</xref>]. At each occasion, we used 14 occasion-specific OHIP items to identify a latent common factor. Additionally, we estimated across-occasion covariances among the latent factors and among the corresponding item residuals (Fig. <xref rid="Fig1" ref-type="fig">1</xref>).<fig id="Fig1"><label>Fig. 1</label><caption><p>One-factor model for OHRQoL assessed with 14-item OHIP at two occasions; <italic>Note</italic>: rectangles represent items (i.e., measured or observed variables [OHIP<sub>2</sub> – OHIP<sub>48</sub>]), ovals reflect latent common factors [OHRQoL<sub>1</sub> and OHRQoL<sub>2</sub>], triangles indicate intercepts [τ<sub>2</sub> − τ<sub>48</sub> and τ<italic>´</italic><sub>2</sub> − τ<italic>´</italic><sub>48</sub>], unidirectional arrows illustrate directional links (i.e., values of regression parameters for factor loadings [λ<sub>2</sub> − λ<sub>48</sub> and λ<italic>´</italic><sub>2</sub> − λ<italic>´</italic><sub>48</sub>] and intercepts), and bidirectional arrows illustrate common factor variances [<italic>Φ</italic><sub>1,1</sub> and <italic>Φ</italic><sub>2,2</sub>] and between-occasion factor covariance [<italic>Φ</italic><sub>1,2</sub>] as well as item residual variances [ <italic>Ω</italic><sub>2_1,1</sub> − <italic>Ω</italic><sub>48_1,1</sub> and <italic>Ω</italic><sub>2_2,2</sub> − <italic>Ω</italic><sub>48_2,2</sub>] and covariances [<italic>Ω</italic><sub>2_1,2</sub> − <italic>Ω</italic><sub>48_1,2</sub>]. For clarity, notation is slightly different than that used in the text</p></caption><graphic xlink:href="12955_2016_492_Fig1_HTML" id="MO1"></graphic></fig></p><p>The covariance structure among the 28 OHIP items (composed of the two sets of OHIP-14 items) was modeled as a two-factor confirmatory factor analysis (CFA).<disp-formula id="Equ1"><label>1</label><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
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\begin{document}$$ \Sigma ={\Gamma \Phi \Gamma}^T+\Omega $$\end{document}</tex-math><mml:math id="M2"><mml:mi mathvariant="normal">Σ</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="normal">Γ</mml:mi><mml:mi mathvariant="normal">Φ</mml:mi><mml:mi mathvariant="normal">Γ</mml:mi></mml:mrow><mml:mi>T</mml:mi></mml:msup><mml:mo>+</mml:mo><mml:mi mathvariant="normal">Ω</mml:mi></mml:math><graphic xlink:href="12955_2016_492_Article_Equ1.gif" position="anchor"></graphic></alternatives></disp-formula>where Σ denotes the model-implied covariance matrix for the two sets of OHIP items; <inline-formula id="IEq1"><alternatives><tex-math id="M3">\documentclass[12pt]{minimal}
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\begin{document}$$ \Gamma =\left(\begin{array}{cc}\hfill {\Gamma}_1\hfill & \hfill 0\hfill \\ {}\hfill 0\hfill & \hfill {\Gamma}_2\hfill \end{array}\right) $$\end{document}</tex-math><mml:math id="M4"><mml:mi mathvariant="normal">Γ</mml:mi><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mtable columnalign="center"><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Γ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mtd><mml:mtd columnalign="center"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:mn>0</mml:mn></mml:mtd><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Γ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mfenced></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq1.gif"></inline-graphic></alternatives></inline-formula> is a 28 × 2 matrix where Γ<sub>1</sub> and Γ<sub>2</sub> denote the occasion-specific factor loadings for the 14 OHIP items (subscripts refer to Time 1 and Time 2, respectively); <inline-formula id="IEq2"><alternatives><tex-math id="M5">\documentclass[12pt]{minimal}
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\begin{document}$$ \Phi =\left(\begin{array}{cc}\hfill {\Phi}_{11}\hfill & \hfill {\Phi}_{12}\hfill \\ {}\hfill {\Phi}_{12}\hfill & \hfill {\Phi}_{22}\hfill \end{array}\right) $$\end{document}</tex-math><mml:math id="M6"><mml:mi mathvariant="normal">Φ</mml:mi><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mtable columnalign="center"><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Φ</mml:mi><mml:mn>11</mml:mn></mml:msub></mml:mtd><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Φ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mtd></mml:mtr><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Φ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mtd><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Φ</mml:mi><mml:mn>22</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mfenced></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq2.gif"></inline-graphic></alternatives></inline-formula> equals the variances and covariances among the common latent factors, where Φ<sub>11</sub> and Φ<sub>22</sub> represent the occasion-specific factor variances, and Φ<sub>22</sub> represents the between-occasion factor covariance; and <inline-formula id="IEq3"><alternatives><tex-math id="M7">\documentclass[12pt]{minimal}
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\begin{document}$$ \Omega =\left(\begin{array}{cc}\hfill {\Omega}_{11}\hfill & \hfill {\Omega}_{12}\hfill \\ {}\hfill {\Omega}_{12}\hfill & \hfill {\Omega}_{22}\hfill \end{array}\right) $$\end{document}</tex-math><mml:math id="M8"><mml:mi mathvariant="normal">Ω</mml:mi><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mtable columnalign="center"><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Ω</mml:mi><mml:mn>11</mml:mn></mml:msub></mml:mtd><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Ω</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mtd></mml:mtr><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Ω</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mtd><mml:mtd columnalign="center"><mml:msub><mml:mi mathvariant="normal">Ω</mml:mi><mml:mn>22</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mfenced></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq3.gif"></inline-graphic></alternatives></inline-formula> denotes the item residual variances and covariances. Note that Φ<sub>11</sub> and Φ<sub>22</sub> are 14 × 14 diagonal matrices representing occasion-specific residual variances, and Φ<sub>12</sub> is a diagonal matrix of across-occasion residual covariances. In our notation, diag(Ω<sub><italic>kl</italic></sub>) denotes the diagonal values of block matrix Ω<sub><italic>kl</italic></sub> (<italic>k</italic> = {1,2}, <italic>l</italic> = {1,2})<italic>.</italic></p><p>Item means were modeled by estimating item intercepts, <italic>τ</italic>, such that<disp-formula id="Equ2"><label>2</label><alternatives><tex-math id="M9">\documentclass[12pt]{minimal}
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\begin{document}$$ \mu \left(\boldsymbol{y}\right)=\left(\begin{array}{c}\hfill {\mu}_1\hfill \\ {}\hfill {\mu}_2\hfill \end{array}\right) $$\end{document}</tex-math><mml:math id="M12"><mml:mi>μ</mml:mi><mml:mfenced close=")" open="("><mml:mi mathvariant="bold-italic">y</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mtable columnalign="center"><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi>μ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mtd></mml:mtr><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi>μ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mfenced></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq4.gif"></inline-graphic></alternatives></inline-formula> and <italic>μ</italic><sub>1</sub> and <italic>μ</italic><sub>2</sub> contain the occasion-specific observed item means; <inline-formula id="IEq5"><alternatives><tex-math id="M13">\documentclass[12pt]{minimal}
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\begin{document}$$ \tau =\left(\begin{array}{c}\hfill {\tau}_1\hfill \\ {}\hfill {\tau}_2\hfill \end{array}\right) $$\end{document}</tex-math><mml:math id="M14"><mml:mi>τ</mml:mi><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mtable columnalign="center"><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi>τ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mtd></mml:mtr><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi>τ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mfenced></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq5.gif"></inline-graphic></alternatives></inline-formula> and <italic>τ</italic><sub>1</sub> and <italic>τ</italic><sub>2</sub> contain the occasion-specific item intercepts; and <inline-formula id="IEq6"><alternatives><tex-math id="M15">\documentclass[12pt]{minimal}
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\begin{document}$$ \alpha =\left(\begin{array}{c}\hfill {\alpha}_1\hfill \\ {}\hfill {\alpha}_2\hfill \end{array}\right) $$\end{document}</tex-math><mml:math id="M16"><mml:mi>α</mml:mi><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mtable columnalign="center"><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi>α</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mtd></mml:mtr><mml:mtr columnalign="center"><mml:mtd columnalign="center"><mml:msub><mml:mi>α</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mfenced></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq6.gif"></inline-graphic></alternatives></inline-formula> is a 2 × 1 vector of latent factor means.</p><p>Due to the small number of OHIP response categories, the item residuals (i.e., the factor uniqueness scores that represent item variance not attributed to a common factor) are unlikely to be normally distributed. Thus it would be inappropriate to estimate the model parameters via maximum likelihood. For this reason, we fit competing CFA models with an unweighted least squares estimator using a mean and variance correction to calculate robust test statistics [<xref ref-type="bibr" rid="CR34">34</xref>].</p></sec><sec id="Sec6"><title>Goodness-of-fit</title><p>To evaluate model fit, we used several goodness-of-fit indices recommended by Kline [<xref ref-type="bibr" rid="CR29">29</xref>], including the log-likelihood chi-square test, the standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the Tucker–Lewis index (TLI). Commonly applied guidelines [<xref ref-type="bibr" rid="CR35">35</xref>] for adequate model fit suggest: SRMR: ≤ .08; RMSEA: ≤ .06; and CFI, TLI: ≥ .95. Accordingly, models not meeting these criteria were rejected.</p></sec><sec id="Sec7"><title>Model specifications for assessment of measurement invariance</title><p>In our first model, we tested whether the data could be characterized by single latent factors for each set of 14 OHIP items. If this model fails to be rejected, we have evidence for dimensional and configural invariance [<xref ref-type="bibr" rid="CR31">31</xref>]. If the model is rejected, we have evidence for reconceptualization [<xref ref-type="bibr" rid="CR30">30</xref>]. In Model 1, factor loadings (Γ<sub>1</sub>, Γ<sub>2</sub>), intercepts (<italic>τ</italic><sub>1</sub>, <italic>τ</italic><sub>2</sub>), and residual variances (diag(Ω<sub>11</sub>), diag(Ω<sub>22</sub>)) were freely estimated for each occasion. This unconstrained model includes the fewest number of parameter restrictions of the models under consideration. All elements of the factor covariance matrix, Φ, were freely estimated to allow the latent factor variances (i.e., the variances of the latent OHRQoL levels) to differ across occasions. For identification purposes, the first elements of Γ<sub>1</sub> and Γ<sub>2</sub> were fixed to 1.00, and the common latent factor means (<italic>α</italic><sub>1</sub> and <italic>α</italic><sub>2</sub>) were fixed to 0.</p><p>Next, we fit a highly constrained model to test for response shifts effects in the across-occasion OHIP scores. In this model, we evaluated the presence of reprioritization and recalibration as operationalized by Oort [<xref ref-type="bibr" rid="CR30">30</xref>]. In this framework, Γ<sub>1</sub> ≠ Γ<sub>2</sub> represents reprioritization, <italic>τ</italic><sub>1</sub> ≠ <italic>τ</italic><sub>2</sub> represents uniform recalibration, and diag(Ω<sub>11</sub>) ≠ diag(Ω<sub>22</sub>) represents non-uniform recalibration. For Model 2, all response shift parameters were constrained by specifying Γ<sub>1</sub> = Γ<sub>2</sub>, <italic>τ</italic><sub>1</sub> = <italic>τ</italic><sub>2</sub>, and diag(Ω<sub>11</sub>) = diag(Ω<sub>22</sub>), representing strict factorial invariance. Latent factor means were not constrained to be equal, <italic>α</italic><sub>1</sub> was fixed to 0, and <italic>α</italic><sub>2</sub> was freely estimated. Once again, to identify the model, the first elements of Γ<sub>1</sub> and Γ<sub>2</sub> were fixed to 1.00. To test for strict factorial invariance, we compared the relative model fit of the unconstrained Model 1 with the constrained Model 2, and tested for statistical significance using chi-square difference tests that were computed using the formulas described in Satorra and Bentler [<xref ref-type="bibr" rid="CR36">36</xref>] for robust, mean and variance scaled chi-squares.</p><p>Finally, we fit a third model, Model 3, that can be viewed as a compromise between the fully unconstrained structure of Model 1 and the highly constrained structure of Model 2. In this model, the residual variances were freely estimated (diag(Ω<sub>11</sub>) ≠ diag(Ω<sub>22</sub>)) to allow for occasion-specific differences in item reliabilities. Once again, for identification purposes, the first elements of Γ<sub>1</sub> and Γ<sub>2</sub> were fixed to 1.00, and <italic>α</italic><sub>1</sub> was fixed to 0.</p></sec><sec id="Sec8"><title>Occasion-specific changes in OHRQoL</title><p>Effect sizes for across-occasion changes in OHRQoL were calculated for the 14 items and the latent factor means. Within the CFA framework outlined by Oort [<xref ref-type="bibr" rid="CR30">30</xref>], across-occasion item mean differences are potentially composed of two components: true changes due to latent factor mean differences and changes due to response shifts. Because Model 3 includes no response shifts due to intercept or loading differences, the observed item changes equal the true item changes. Let<disp-formula id="Equ3"><label>3</label><alternatives><tex-math id="M17">\documentclass[12pt]{minimal}
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\begin{document}$$ \widehat{\Sigma}=\widehat{\Gamma}\widehat{\Phi}{\widehat{\Gamma}}^T+\widehat{\Omega} $$\end{document}</tex-math><mml:math id="M18"><mml:mover accent="true"><mml:mi mathvariant="normal">Σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mover accent="true"><mml:mi mathvariant="normal">Γ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mover accent="true"><mml:mi mathvariant="normal">Φ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:msup><mml:mover accent="true"><mml:mi mathvariant="normal">Γ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mi>T</mml:mi></mml:msup><mml:mo>+</mml:mo><mml:mover accent="true"><mml:mi mathvariant="normal">Ω</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover></mml:math><graphic xlink:href="12955_2016_492_Article_Equ3.gif" position="anchor"></graphic></alternatives></disp-formula>denote the estimated parameters of EQ(<xref rid="Equ1" ref-type="">1</xref>) and let <inline-formula id="IEq7"><alternatives><tex-math id="M19">\documentclass[12pt]{minimal}
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\begin{document}$$ {\widehat{\sigma}}_{jk} $$\end{document}</tex-math><mml:math id="M20"><mml:msub><mml:mover accent="true"><mml:mi>σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq7.gif"></inline-graphic></alternatives></inline-formula> be the row <italic>j</italic>, column <italic>k</italic> element of <inline-formula id="IEq8"><alternatives><tex-math id="M21">\documentclass[12pt]{minimal}
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\begin{document}$$ \widehat{\Sigma} $$\end{document}</tex-math><mml:math id="M22"><mml:mover accent="true"><mml:mi mathvariant="normal">Σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq8.gif"></inline-graphic></alternatives></inline-formula> (i.e., the reproduced covariance matrix for the 28 OHIP items) such that <inline-formula id="IEq9"><alternatives><tex-math id="M23">\documentclass[12pt]{minimal}
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\begin{document}$$ {\widehat{\sigma}}_{ii} $$\end{document}</tex-math><mml:math id="M24"><mml:msub><mml:mover accent="true"><mml:mi>σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mrow><mml:mi>i</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq9.gif"></inline-graphic></alternatives></inline-formula> denotes the estimated variance for item <italic>i</italic>(<italic>i</italic> = 1, …, 28). Given the parameter estimates in EQ(<xref rid="Equ3" ref-type="">3</xref>), the <italic>i</italic><sup><italic>th</italic></sup> (<italic>i</italic> = 1, …, 14) true item-change effect size equals <inline-formula id="IEq10"><alternatives><tex-math id="M25">\documentclass[12pt]{minimal}
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\begin{document}$$ \left({\mu}_{1(i)}-{\mu}_{2(i)}\right)/\sqrt{{\widehat{\sigma}}_{ii}+{\widehat{\sigma}}_{\left(i+14\right)\left(i+14\right)}-2{\widehat{\sigma}}_{\left(i+14\right)i}} $$\end{document}</tex-math><mml:math id="M26"><mml:mfenced close=")" open="("><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mfenced close=")" open="("><mml:mi>i</mml:mi></mml:mfenced></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mfenced close=")" open="("><mml:mi>i</mml:mi></mml:mfenced></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo stretchy="true">/</mml:mo><mml:msqrt><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mrow><mml:mi>i</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mover accent="true"><mml:mi>σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mrow><mml:mfenced close=")" open="("><mml:mrow><mml:mi>i</mml:mi><mml:mo>+</mml:mo><mml:mn>14</mml:mn></mml:mrow></mml:mfenced><mml:mfenced close=")" open="("><mml:mrow><mml:mi>i</mml:mi><mml:mo>+</mml:mo><mml:mn>14</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mover accent="true"><mml:mi>σ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mrow><mml:mfenced close=")" open="("><mml:mrow><mml:mi>i</mml:mi><mml:mo>+</mml:mo><mml:mn>14</mml:mn></mml:mrow></mml:mfenced><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msqrt></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq10.gif"></inline-graphic></alternatives></inline-formula>, where <italic>μ</italic><sub>1(<italic>i</italic>)</sub> denotes the <italic>i</italic><sup><italic>th</italic></sup> item mean at Time 1 and <italic>μ</italic><sub>2(<italic>i</italic>)</sub> denotes the associated mean at Time 2. Finally, the estimated latent factor effect size equals <inline-formula id="IEq11"><alternatives><tex-math id="M27">\documentclass[12pt]{minimal}
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\begin{document}$$ \left({\widehat{\alpha}}_2-{\widehat{\alpha}}_1\right)/\sqrt{{\widehat{\Phi}}_{11}} $$\end{document}</tex-math><mml:math id="M28"><mml:mfenced close=")" open="("><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>α</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mover accent="true"><mml:mi>α</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mfenced><mml:mo stretchy="true">/</mml:mo><mml:msqrt><mml:msub><mml:mover accent="true"><mml:mi mathvariant="normal">Φ</mml:mi><mml:mo stretchy="true">^</mml:mo></mml:mover><mml:mn>11</mml:mn></mml:msub></mml:msqrt></mml:math><inline-graphic xlink:href="12955_2016_492_Article_IEq11.gif"></inline-graphic></alternatives></inline-formula>. A nonparametric bootstrap, using 10,000 samples, yielded 95 % effect size confidence intervals (CIs).</p><p>The latent change effect size for the factor means was compared to the effect size for the OHIP-14 summary scores. According to Cohen [<xref ref-type="bibr" rid="CR37">37</xref>], an effect size of <italic>d</italic> = .2 is small, .5 is medium, and .8 is large. See the Additional file <xref rid="MOESM1" ref-type="media">1</xref> for additional analyses and results regarding item-level reliability.</p><p>Computations were performed with STATA [<xref ref-type="bibr" rid="CR38">38</xref>] and R [<xref ref-type="bibr" rid="CR39">39</xref>]. All structural equation models were fit using the lavaan package [<xref ref-type="bibr" rid="CR40">40</xref>] for R. Statistical significance was based on two-sided tests with Type I error rates set at .05 without adjustments for multiple comparisons.</p></sec></sec><sec id="Sec9"><title>Results</title><sec id="Sec10"><title>Characteristics of participants</title><p>A total of 554 prosthodontic patients with valid data for baseline (Time 1) and follow-up (Time 2) assessments were included in our analyses (Table <xref rid="Tab1" ref-type="table">1</xref>). Mean OHIP summary scores decreased significantly from Time 1 to Time 2 in all study-specific samples (all <italic>p</italic> < .05; Table <xref rid="Tab1" ref-type="table">1</xref>), corresponding to an increase in OHRQoL following prosthodontic treatment. Furthermore, most standard deviations (SDs) were lower at Time 2 than at Time 1, indicating lower score variability at follow-up. Consistent with these findings, all OHIP-14 item means and SDs decreased from Time 1 to Time 2 (Table <xref rid="Tab2" ref-type="table">2</xref>).<table-wrap id="Tab1"><label>Table 1</label><caption><p>Demographic characteristics and OHRQoL change from Time 1 (baseline) to Time 2 (follow-up) of study participants</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="3"></th><th rowspan="3">N</th><th rowspan="2">Age [yrs]</th><th rowspan="2">Female</th><th colspan="3">OHIP-14 sum score</th></tr><tr><th>Time 1</th><th>Time 2</th><th></th></tr><tr><th><italic>Mean (SD)</italic></th><th><italic>n (%)</italic></th><th colspan="2"><italic>Mean (SD)</italic></th><th><italic>P-value*</italic></th></tr></thead><tbody><tr><td>All</td><td>554</td><td>55.3 (15.3)</td><td>286 (51.6)</td><td>10.5 (9.9)</td><td>7.2 (8.0)</td><td>< .001</td></tr><tr><td colspan="7">Included samples</td></tr><tr><td> Hungary [<xref ref-type="bibr" rid="CR15">15</xref>]</td><td>62</td><td>54.9 (14.6)</td><td>37 (59.7)</td><td>13.2 (10.7)</td><td>6.8 (10.9)</td><td>< .001</td></tr><tr><td> Germany [<xref ref-type="bibr" rid="CR16">16</xref>]</td><td>208</td><td>55.7 (15.8)</td><td>98 (47.1)</td><td>8.0 (7.3)</td><td>7.0 (6.8)</td><td>.005</td></tr><tr><td> Germany [<xref ref-type="bibr" rid="CR17">17</xref>]</td><td>101</td><td>55.9 (14.6)</td><td>56 (55.4)</td><td>11.2 (10.6)</td><td>5.7 (8.4)</td><td>< .001</td></tr><tr><td> Germany [<xref ref-type="bibr" rid="CR8">8</xref>]</td><td>123</td><td>54.6 (15.8)</td><td>61 (49.6)</td><td>8.0 (8.8)</td><td>6.6 (7.7)</td><td>.009</td></tr><tr><td> Japan [<xref ref-type="bibr" rid="CR18">18</xref>]</td><td>30</td><td>60.8 (14.4)</td><td>23 (76.7)</td><td>16.0 (10.3)</td><td>10.8 (8.8)</td><td>< .001</td></tr><tr><td> Croatia (not published yet)</td><td>30</td><td>48.1 (12.7)</td><td>11 (36.7)</td><td>25.4 (8.1)</td><td>12.6 (4.8)</td><td>< .001</td></tr></tbody></table><table-wrap-foot><p>*Paired <italic>t</italic> test</p></table-wrap-foot></table-wrap><table-wrap id="Tab2"><label>Table 2</label><caption><p>OHIP-14 item content and item means with standard deviations at Time 1 (baseline) and Time 2 (follow-up) based on ordinal 5-point response categories</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="3">Item #</th><th rowspan="3">Item content</th><th colspan="2">Item mean (SD)</th></tr><tr><th>Time 1</th><th>Time 2</th></tr><tr><th><italic>N</italic> = 554</th><th><italic>N</italic> = 554</th></tr></thead><tbody><tr><td>Item 2</td><td>Trouble pronouncing words</td><td>0.73 (1.07)</td><td>0.55 (0.81)</td></tr><tr><td>Item 6</td><td>Taste worse</td><td>0.56 (0.89)</td><td>0.45 (0.74)</td></tr><tr><td>Item 10</td><td>Painful aching</td><td>1.05 (1.07)</td><td>0.82 (0.91)</td></tr><tr><td>Item 16</td><td>Uncomfortable to eat</td><td>1.19 (1.28)</td><td>0.86 (1.01)</td></tr><tr><td>Item 20</td><td>Self-conscious</td><td>1.07 (1.33)</td><td>0.57 (0.90)</td></tr><tr><td>Item 23</td><td>Tense</td><td>0.97 (1.12)</td><td>0.61 (0.85)</td></tr><tr><td>Item 29</td><td>Diet unsatisfactory</td><td>0.68 (1.08)</td><td>0.46 (0.78)</td></tr><tr><td>Item 32</td><td>Interrupt meals</td><td>0.71 (1.03)</td><td>0.50 (0.82)</td></tr><tr><td>Item 35</td><td>Difficult to relax</td><td>0.81 (1.09)</td><td>0.51 (0.80)</td></tr><tr><td>Item 38</td><td>Been embarrassed</td><td>0.84 (1.07)</td><td>0.48 (0.76)</td></tr><tr><td>Item 42</td><td>Irritable with others</td><td>0.44 (0.75)</td><td>0.34 (0.63)</td></tr><tr><td>Item 43</td><td>Difficulty doing jobs</td><td>0.43 (0.73)</td><td>0.32 (0.63)</td></tr><tr><td>Item 47</td><td>Life unsatisfying</td><td>0.78 (0.99)</td><td>0.48 (0.78)</td></tr><tr><td>Item 48</td><td>Unable to function</td><td>0.28 (0.62)</td><td>0.23 (0.55)</td></tr></tbody></table></table-wrap></p></sec><sec id="Sec11"><title>Measurement models</title><p>Our initial SEM analysis supported Model 1 (Table <xref rid="Tab3" ref-type="table">3</xref>) and suggested that the data were well characterized by a unidimensional model at each occasion. Thus we found support for configural invariance and no evidence for reconceptualization.<table-wrap id="Tab3"><label>Table 3</label><caption><p>SEM Model fit summary</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Model</th><th>Specifications</th><th>Scaled <italic>χ</italic><sup>2</sup></th><th>df</th><th>Scaled RMSEA</th><th>Scaled SRMR</th><th>Scaled CFI</th><th>Scaled TLI</th></tr></thead><tbody><tr><td># 1</td><td>Γ<sub>1</sub> ≠ Γ<sub>2</sub>, <italic>τ</italic><sub>1</sub> ≠ <italic>τ</italic><sub>2</sub>, Ω<sub>11</sub> ≠ Ω<sub>22</sub></td><td>606</td><td>335</td><td>.038</td><td>.051</td><td>.92</td><td>.91</td></tr><tr><td># 2</td><td>Γ<sub>1</sub> = Γ<sub>2</sub>, <italic>τ</italic><sub>1</sub> = <italic>τ</italic><sub>2</sub>, Ω<sub>11</sub> = Ω<sub>22</sub></td><td>816</td><td>375</td><td>.046</td><td>.078</td><td>.87</td><td>.87</td></tr><tr><td># 3</td><td>Γ<sub>1</sub> = Γ<sub>2</sub>, <italic>τ</italic><sub>1</sub> = <italic>τ</italic><sub>2</sub>, Ω<sub>11</sub> ≠ Ω<sub>22</sub></td><td>633</td><td>361</td><td>.037</td><td>.064</td><td>.92</td><td>.92</td></tr></tbody></table><table-wrap-foot><p>RMSEA - root mean square error of approximation; SRMR - standardized root mean square residual; CFI - comparative fit index; TLI - Tucker–Lewis index</p></table-wrap-foot></table-wrap></p><p>Fit statistics for Model 2 indicated that this model was not a viable structural candidate for the data as the additional model constraints resulted in significantly poorer model fit compared to Model 1 (<italic>χ</italic><sup>2</sup>(40) = 267, <italic>p</italic> <.01). Accordingly, a model enforcing strict factorial invariance and no response shift effects was not supported.</p><p>Model 3 fit considerably better than Model 2 (<italic>χ</italic><sup>2</sup>(14) = 246, <italic>p</italic> <.01) but less well than Model 1 (<italic>χ</italic><sup>2</sup>(26) = 84, <italic>p</italic> <.01). Notice, however, that according to our suite of fit indices, there are trivial differences between Model 1 and the more parsimonious Model 3. For these reasons, we retained Model 3 as the most parsimonious and interpretable structure for the 2-occasion OHIP data. The final parameter estimates for Model 3 are shown in Table <xref rid="Tab4" ref-type="table">4</xref>. As expected, item residual variances were lower for Time 2 (diag(Ω<sub>22</sub>)) than for Time 1 (diag(Ω<sub>11</sub>)). Whereas there was no evidence for the presence of reprioritization and uniform recalibration, changes in residual variances suggested non-uniform recalibration in the measurement model.<table-wrap id="Tab4"><label>Table 4</label><caption><p>Parameter estimates for final model (# 3) and effect sizes of observed changes based on ordinal 5-point response categories and of true changes when item means were modeled by specifying a vector of model intercepts in final CFA model</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Item #</th><th colspan="5">Parameter estimates<sup>a</sup></th><th colspan="2">Effect sizes</th></tr><tr><th></th><th><italic>Γ</italic><sub>1</sub> = <italic>Γ</italic><sub>2</sub></th><th><italic>τ</italic><sub>1</sub> = <italic>τ</italic><sub>2</sub></th><th><italic>diag</italic>(<italic>Ω</italic><sub>12</sub>)</th><th><italic>diag</italic>(<italic>Ω</italic><sub>11</sub>)</th><th><italic>diag</italic>(<italic>Ω</italic><sub>22</sub>)</th><th>Observed item changes</th><th>True item changes</th></tr></thead><tbody><tr><td>Item 2</td><td>1.000</td><td>0.762</td><td>.155</td><td>.701</td><td>.365</td><td>−.18</td><td>−.24</td></tr><tr><td>Item 6</td><td>0.731</td><td>0.594</td><td>.217</td><td>.565</td><td>.403</td><td>−.14</td><td>−.22</td></tr><tr><td>Item 10</td><td>0.872</td><td>1.042</td><td>.172</td><td>.816</td><td>.618</td><td>−.21</td><td>−.19</td></tr><tr><td>Item 16</td><td>1.439</td><td>1.202</td><td>.085</td><td>.731</td><td>.438</td><td>−.26</td><td>−.28</td></tr><tr><td>Item 20</td><td>1.394</td><td>0.993</td><td>.072</td><td>.903</td><td>.257</td><td>−.41</td><td>−.28</td></tr><tr><td>Item 23</td><td>1.236</td><td>0.942</td><td>.023</td><td>.570</td><td>.295</td><td>−.33</td><td>−.27</td></tr><tr><td>Item 29</td><td>1.188</td><td>0.719</td><td>.119</td><td>.550</td><td>.206</td><td>−.23</td><td>−.31</td></tr><tr><td>Item 32</td><td>1.047</td><td>0.735</td><td>.048</td><td>.579</td><td>.358</td><td>−.20</td><td>−.24</td></tr><tr><td>Item 35</td><td>1.010</td><td>0.784</td><td>.168</td><td>.736</td><td>.351</td><td>−.29</td><td>−.25</td></tr><tr><td>Item 38</td><td>1.219</td><td>0.811</td><td>.005</td><td>.484</td><td>.160</td><td>−.35</td><td>−.30</td></tr><tr><td>Item 42</td><td>0.752</td><td>0.480</td><td>.088</td><td>.308</td><td>.233</td><td>−.14</td><td>−.26</td></tr><tr><td>Item 43</td><td>0.787</td><td>0.471</td><td>.058</td><td>.265</td><td>.227</td><td>−.14</td><td>−.26</td></tr><tr><td>Item 47</td><td>1.064</td><td>0.760</td><td>.140</td><td>.490</td><td>.287</td><td>−.34</td><td>−.29</td></tr><tr><td>Item 48</td><td>0.600</td><td>0.332</td><td>.092</td><td>.222</td><td>.203</td><td>−.09</td><td>−.25</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>OHIP-14 sum score</td><td>Latent factor mean</td></tr><tr><td></td><td><italic>α</italic><sub>1</sub></td><td><italic>α</italic><sub>2</sub></td><td><italic>Φ</italic><sub>12</sub></td><td><italic>Φ</italic><sub>11</sub></td><td><italic>Φ</italic><sub>22</sub></td><td>Observed change</td><td>True change</td></tr><tr><td></td><td>.000</td><td>−.246</td><td>.229</td><td>.441</td><td>.284</td><td>−.34</td><td>−.37</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Subscripts refer to Time 1 and Time 2, respectively</p><p><italic>Γ</italic>: factor loadings; τ: item intercepts; <italic>Ω</italic>: across-occasion and occasion-specific residual variances; α: latent factor means; <italic>Φ</italic>: variances and covariances among the common latent factors</p><p>Note: For the factor loadings (<italic>Γ</italic>), standard errors were ≤ .095. For the intercepts (τ), standard errors were ≤ .049. For the residual covariances (diag(Ω<sub>12</sub>)), standard errors were ≤ .039. For the residual variances at Time 1 (diag(Ω<sub>11</sub>)), standard errors were ≤ .067; at time 2 (diag(Ω<sub>22</sub>)), standard errors were ≤ .054. The standard error of <italic>α</italic><sub>2</sub> equals .029; the standard errors of <italic>Φ</italic><sub>12</sub>, <italic>Φ</italic><sub>11</sub>, and <italic>Φ</italic><sub>22</sub> equal .033, .058, and .042, respectively</p></table-wrap-foot></table-wrap></p></sec><sec id="Sec12"><title>Observed and true changes in OHRQoL</title><p>As shown in Table <xref rid="Tab4" ref-type="table">4</xref>, effect sizes for the observed item changes ranged from -.09 (Item 48) to -.41 (Item 20) and the effect sizes for the true item changes ranged from -.19 (Item 10) to -.31 (Item 29). Although the observed and true item effect sizes differed, the differences were generally small with no discernable pattern.</p><p>The effect size of the latent common factor change was -.37 (95 % CI: -.43 to -.31). This estimate suggests that the average Time 2 common factor score was .37 standard deviations lower than the average Time 1 common factor score. The effect size of the average OHIP-14 summary score was -.34 (95 % CI: -.42 to -.26), and not substantially different than the effect size of the latent factor.</p></sec></sec><sec id="Sec13"><title>Discussion</title><p>Longitudinal measurement invariance of the OHIP was assessed with SEM to elucidate potential changes in across-occasion measurement models of OHRQoL. Data were well characterized by a model that included occasion-specific, single factor OHRQoL dimensions. On the basis of several goodness of fit statistics and model parsimony considerations, the data supported a model that specified across-occasion measurement invariance of the OHIP-14 latent structure. Hence, the results of this international study of OHRQoL suggest that the biasing effects of response shift [<xref ref-type="bibr" rid="CR30">30</xref>] on OHIP scores is minimal.</p><p>As a measure of OHRQoL, the OHIP putatively reflects the theoretical structure of patient-perceived oral health across populations and different occasions. In the presence of response shift, changes in OHIP scores would not only represent true changes in the underlying OHRQoL construct. Rather, such observed changes would reflect changes in the measurement models. Because OHRQoL is a dynamic construct [<xref ref-type="bibr" rid="CR41">41</xref>], the measurement model for this construct may change over time. However, the only change in the retained measurement model of the present study was in the item residual variances, that is, in the parts of the item variances that could not be attributed to the occasion-specific OHRQoL common factor. According to Oort’s [<xref ref-type="bibr" rid="CR30">30</xref>] model this result reflects non-uniform recalibration. However, since this is a prospective cohort study with prosthodontic treatment between assessments, across-occasion changes in item residual variances seem not to be indicative of non-uniform recalibration. Specifically, because item means and SDs decrease from baseline to follow-up as an effect of treatment, residuals variances should also decrease as the item means approach their lower bounds. When treatment is maximally effective, all problems disappear, resulting in items means and variances of zero. Consequently, residual variances should also approach zero under ideal conditions of clinical improvement. Hence reduced item residual variances at Time 2 were expected due to post-treatment reduction in the number of oral health problems. Thus, our findings provide no evidence for significant response shift effects in prospective OHRQoL assessments using the OHIP in prosthodontic patients.</p><p>To our knowledge, this is the first study to apply SEM to response shift measurement in prospective OHRQoL assessments using the OHIP. Hence our ability to compare our findings with those in the existing literature is limited. Previous studies in dentistry have consistently reported response shift effects in the assessment of change scores [<xref ref-type="bibr" rid="CR7">7</xref>–<xref ref-type="bibr" rid="CR9">9</xref>]. All of these studies were prospective intervention studies with various types of prosthodontic treatments performed between baseline and follow-up. A general finding from this body of work is that treatment effects were larger when response shift was taken into account. Furthermore, several medical studies also demonstrated response shift effects with larger changes in health-related quality of life when considering response shift [<xref ref-type="bibr" rid="CR4">4</xref>, <xref ref-type="bibr" rid="CR5">5</xref>]. This is in contrast to findings of no substantial response shift effects in the present study. Since different methods exist to detect response shift in patient-reported measures [<xref ref-type="bibr" rid="CR2">2</xref>], inconsistencies among findings might be due to study design (prospective or retrospective). Furthermore, it is assumed that the occurrence of response shift depends on the presence of a catalyst [<xref ref-type="bibr" rid="CR6">6</xref>], with medic al treatment being an important example. When no potential catalyst is present, that is, in individuals with chronic conditions who are in stable health, no substantial response shift effects exist [<xref ref-type="bibr" rid="CR42">42</xref>]. Even though all patients in the present study received prosthodontic treatments that substantially improved their perceived oral health, this treatment-induced change in oral health might not have been large enough to catalyze changes in patients’ internal standards. This does not necessarily mean that prosthodontic treatment is not a catalyst in this context, but our data provide evidence that its effect on OHIP scores in terms of response shift is not clinically relevant.</p><p>This study has strengths and limitations. We applied state of the art CFA models to assess measurement invariance in prospective OHRQoL assessment. Although these methods have not been applied in dentistry often, they are well established in other medical fields [<xref ref-type="bibr" rid="CR30">30</xref>] and in psychometrics [<xref ref-type="bibr" rid="CR31">31</xref>]. The most commonly used approach to test for response shift or measurement invariance is the then-test method [<xref ref-type="bibr" rid="CR2">2</xref>], which requires that the patients retrospectively rate their QoL at baseline from the perspective at follow-up. In contrast to the then-test method, SEM does not require multiple assessments at each occasion. Other advantages of our approach over the then-test is that our results are not susceptible to recall bias [<xref ref-type="bibr" rid="CR4">4</xref>, <xref ref-type="bibr" rid="CR43">43</xref>] or to confounders that are attributable to “implicit theory of change” or “cognitive dissonance theory” [<xref ref-type="bibr" rid="CR44">44</xref>, <xref ref-type="bibr" rid="CR45">45</xref>]. Although we cannot completely rule out these confounders, any confounding effects should be low or negligible due to the large time periods between baseline and follow-up assessments. For example, in one of the included studies [<xref ref-type="bibr" rid="CR8">8</xref>], the between assessment time intervals averaged four months. Accordingly, baseline status should have no meaningful impact on follow-up information in a prospective assessment. When using SEM, we were able to quantify the stability or robustness of the theoretical structure of patient-perceived oral health across occasions. Using this approach, as opposed to the then-test, we were also able to evaluate the critically important property of across-occasion measurement invariance. Although we used only data from two occasions in the included studies, our findings should generalize to longitudinal studies with three or more assessments when no potential catalyst is present between assessments.</p><p>As noted earlier, our SEM analyses provided cogent evidence that OHIP-14 scores are well-characterized by a unidimensional measurement model. Given this result, we could not test for configural invariance separately from dimensional invariance. However, the one-factorial structure of OHRQoL assessed with OHIP has been corroborated in previous EFA and CFA analyses [<xref ref-type="bibr" rid="CR32">32</xref>, <xref ref-type="bibr" rid="CR33">33</xref>], and our data fit the unconstrained single factor model for each occasion very well. Thus, our findings support both dimensional (same number of common factors) and configural invariance (common factors associated with identical items) for the OHIP short form. We used OHIP-14 as this is one of the most commonly applied OHRQoL questionnaires, with sufficient psychometric properties and less administrative burden than the longer versions [<xref ref-type="bibr" rid="CR24">24</xref>, <xref ref-type="bibr" rid="CR46">46</xref>–<xref ref-type="bibr" rid="CR49">49</xref>], making our findings relevant for most OHIP research.</p><p>This study used pooled data from several international studies to create stable models with precise parameter estimates. The included samples consisted of patients in university-based prosthodontic departments and did not differ substantially in age, gender, or perceived improvements in OHRQoL following prosthodontic treatment. Furthermore, we found no signs that cross-cultural measurement invariance was violated, which is in line with a previous study in a similar setting [<xref ref-type="bibr" rid="CR50">50</xref>]. Because patients in this study were typical dental patients [<xref ref-type="bibr" rid="CR11">11</xref>], our findings should generalize well to other dental patient populations.</p></sec><sec id="Sec14"><title>Conclusions</title><p>In conclusion, this study clearly demonstrated that patients’ observed changes in perceived oral health are not confounded by response shift effects in the measurement of OHRQoL using the OHIP-14. In other words, changes in OHIP-14 mean scores due to treatment can be trusted to reflect true change in patients’ OHRQoL.</p></sec><sec id="Sec15"><title>Abbreviations</title><p>CFA, Confirmatory factor analysis; CFI, Comparative fit index; DOQ, Dimensions of Oral Health-Related Quality of Life; OHIP, Oral Health Impact Profile; OHRQoL, Oral health-related quality of life; RMSEA, Root mean square error of approximation; SEM, Structural equation model; SRMR, Standardized root mean square residual; TLI, Tucker–Lewis index.</p></sec></body><back><app-group><app id="App1"><sec id="Sec16"><title>Additional file</title><p><media position="anchor" xlink:href="12955_2016_492_MOESM1_ESM.docx" id="MOESM1"><label>Additional file 1:</label><caption><p>Item-level reliability. (DOCX 101 kb)</p></caption></media></p></sec></app></app-group><ack><title>Acknowledgements</title><p>We are grateful to Ms. Andrea Medina (University of Minnesota) for her valuable comments on an earlier version of the manuscript.</p><p>Research reported in this publication was supported by the National Institute of Dental and Craniofacial Research of the National Institutes of Health (USA) under Award Number R01DE022331 and by the German Research Foundation (Germany) under Award Number RE 3289/2-1.</p><sec id="FPar1"><title>Authors’ contribution</title><p>All authors participated in the design and coordination of the study. DRR, MTJ, LF, and NW performed the statistical analyses. DRR drafted the manuscript with the help of MTJ, LF, and NW. KB, GZ, and AČ have contributed in the interpretation of the data and results of the statistical analyses, and have critically revised the paper. 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